Awesome GitHub RepositoriesLanguage Diffusion Models

LLaDA is a masked diffusion language model and conditional text generator. It generates text by iteratively refining masked tokens through a diffusion process rather than predicting the next token in a sequence. The project functions as a vision-language diffusion model, converting visual inputs into text responses. It also serves as a preference optimization framework that uses log-likelihood estimation and evidence lower bounds to tune model responses. The system supports multi-round conversational AI and text sequence evaluation. It integrates vision-language embedding for cross-modal con

dLLM: Simple Diffusion Language Modeling

](https://huggingface.co/Dream-org/Dream-v0-Base-7B)

By Marianne Arriola, Aaron Gokaslan, Justin T Chiu, Zhihan Yang, Zhixuan Qi, Jiaqi Han, Subham Sekhar Sahoo, Volodymyr Kuleshov

This repo contains a PyTorch implementation for the paper Discrete Diffusion Modeling by Estimating the Ratios of the Data Distribution by Aaron Lou, Chenlin Meng and Stefano Ermon.

By Subham Sekhar Sahoo, Marianne Arriola, Yair Schiff, Aaron Gokaslan, Edgar Marroquin, Justin T Chiu, Alexander Rush, Volodymyr Kuleshov

Current Diffusion Language Models (DLMs) have been studied at a smaller scale compared to their autoregressive (AR) counterparts and lack fair comparison on language modeling benchmarks. Additionally, training diffusion models from scratch at scale remains challenging. We propose adapting…

Masked diffusion models (MDMs) have shown promise in language modeling, yet their scalability and effectiveness in core language tasks, such as text generation and language understanding, remain underexplored. This paper establishes the first scaling law for MDMs, demonstrating a scaling rate…

We introduce SDAR (Synergy of Diffusion and AutoRegression), a large-scale diffusion language model that unites the complementary strengths of autoregressive and discrete diffusion modeling. By merging the training efficiency of autoregressive methods with the highly parallel decoding ability of…

Official implementation of DiffusionBERT: Improving Generative Masked Language Models with Diffusion Models.

MegaDLMs

By Subham Sekhar Sahoo, Justin Deschenaux, Aaron Gokaslan, Guanghan Wang, Justin Chiu, Volodymyr Kuleshov

Diffusion Language Models are Super Data Learners

please use requirementsgpu.txt if your accelerator is GPUs, use requirementstpu.txt when using Google Cloud TPUs.

By Dimitri von Rütte, Janis Fluri, Yuhui Ding, Antonio Orvieto, Bernhard Schölkopf, Thomas Hofmann

](https://huggingface.co/Dream-org/DreamOn-v0-7B)

Subham Sekhar Sahoo \ 1 , Zhihan Yang \ 2 , Yash Akhauri †1 , Johnna Liu †1 , Deepansha Singh †1 , Zhoujun Cheng †3 , Zhengzhong Liu 3 , Eric Xing 3 , John Thickstun 2 , Arash Vahdat 4

This repository contains code for training and evaluating the models in the paper Likelihood-Based Diffusion Language Models.

This repository contains the official implementation of paper A Reparameterized Discrete Diffusion Model for Text Generation.